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Paper presentation: Neural networks and Face recognition

Abstract of this computer paper-presentations : (Neural networks and Face recognition)

Face Recognition is the inherent capability of human beings. Identifying a person by face is one of the most fundamental human functions since time immemorial. Face recognition by computer is to endow a machine with capability to approximate in some sense, a similar capability in human beings. To impart this basic human capability to a machine has been a subject of interest over the last few years. Such a machine would find considerable utility in many commercial transactions, personal management, security and the law enforcement applications, especially in criminal identification, authentication in secure system etc. Enough research has not been carried out on identification of human faces. However a number of automated or semi-automated recognition studies have been reported.

Artificial Neural Networks (ANN) is an attempt to simulate human brain; hence this method is named artificial neural networks. Neural networks, which are inspired from the studies of biological nervous systems, have recently been used for various applications, due to the distributed computing fashion over a large number of simple processing units (Neurons). These neurons of nodes, which are simple, non-linear computational elements, are connected by links with variable weights. The inherent parallelism of these networks provides high computational rates with greater degree of robustness or fault tolerance than conventional computers. The fault tolerance is due to the presence of many processing nodes, each of which is responsible for a small portion of the task. Damage to a few nodes or links, doesn’t impair overall performance significantly.

Recognition is regarded as a basic attribute of human beings, as well as other living organisms. A pattern is the description of an object to be recognized. Recognition of concrete pattern by human beings may be considered as psycho physiological problem, which involve a relationship between persons and physical stimulus. When a person received or pattern, he makes an inductive inference and associate this perception with some general concepts which he has derived from his prior experience. Human recognition is really a question of estimating the relative odds that the input data can be associated with one of the known set statistical objects, which depend on our past experience and which from clues and past information for recognition.

Thus the problem of pattern recognition may be regarded as one of the discrimination the input data, not between individual patterns, but between different patterns, via the search for features or invariant attributes among members of population.
The basic aim is to make machine work as human being, i.e., development of theory and techniques for the design of devices capable of performing a given recognition may be traced to the early 1950’s. when the digital computer first became a readily available information – processing tool.

Face recognition is one of the pattern recognition systems. Pattern recognition can be defined as the categorization of input data into identifiable classes via the extraction of significant features or attributes of the data from the background of irrelevant details

Artificial neural networks are relatively crude electronic model based on the neural structure of the brain. An initial understanding of the natural thinking mechanism shows that the brains store information as patterns. This process of storing information as patterns, utilizing those patterns and then solving problems encompasses a Neural Network. This field also utilizes words very different from traditional computing, words like behave, react, self-organize, learns generalize and forget

Neural networks can be brained to identify correlative patterns between the input and target values and can subsequently outcomes from new input conditions. Neural networks generally consist of a number of interconnected processing elements or neurons, how the inter-neuron connections are arranged and the nature of the connections determines the structure of the networks.

Identifying a person by his face is one of the most fundamental human functions since time immemorial. To impart this basic human capability to machine has been a subject of interest over the last few years. Such a machine would find considerable utility in many commercial tractions, personnel management and security and law enforcement application, specially in criminal identification, authentication in secure system etc. Enough research has not been carried out on identification of human face. Recently however a number of automated recognition are mainly two folds.

• Large number of facial patterns (faces to be recognized are finitely very large) contrary to many pattern recognition problems where the numbers of pattern classes are finite.

• The dissimilarity amongst the facial patterns is inherently very small.

Recent research effort has been directed towards the extraction of features from the frontal facial photographs of human and its economical use in machine identification of human faces. This strategy best suited is to get the outline of the profile and extract discrete features from it. This technique has been used by L.D. Hardmonetal for the recognition of human faces.


Looking at the side profile of the human face, certain points can be readily selected on the face profile, which when correctly identified may help in extracting certain characteristics features for that particular face. Out of these, five facial marks are independent of each other, while point no.

(3) FOREHEAD POINT is a reflection of point no. (2) CHIN POINT. Through point no. (1) NOSE POINT.

Making of this point helps identifying the start of the profile. It is seen that these points do not change with age. Therefore, five points have been selected for the extraction of various feature measurements for identification purposes. These points have been named as under.

  1. Nose point
  2. Chin point
  3. Forehead point
  4. Bridge point
  5. Upper lip point

It may be seen that the point 5 is soft tissue point and it is rather difficult to extract them accurately. This position will be dependent on the facial expression of the person at the time when photographs is taken (i.e. smiling, laughing, frowning).


• A set of 12 features vectors have been extracted from each facial pattern and the training of the neutral network is carried out with a set of four (4) facial photographs. Thus the network is configured with 12 input nodes and 4 output nodes.

• Two network topologies are used one the BP not having input to hidden layer and hidden to output layer connections and the IO net with additional direct input to output connection.

• Instead of training the network with 12 dimensional feature vector directly, we have used a different feature vector.

• This was done because it was observed that the network was more stable when trained with this differential data rather than the absolute values.

• The order of variation in facial features is very small when compared to the absolute values hence the network cannot differentiate between feature vectors if the absolute values are chosen.

• This clearly shows that the network is dependent on the nature of input data and thus pre-processing is an essential step for neural classification


1. First we have to take the BMP image which is to be recognized.

2. The BMP image is converted into the RAW image.

3. From the RAW image we would calculate six facial distances and six facial angles. Normalize these twelve inputs.

4. These collection of Twelve input’s called Twelve input parameters. Then training starts.

5. From these twelve inputs the weights to the links between the Input layer and the Hidden layer are calculated. these Are stored in matrix 12*7 called Random Matrix W.

6. From these twelve inputs and weights we found out Hidden layer parameters are calculated.

7. Similarly from Hidden layer parameters the weights to the links between hidden layer and output layer are calculated. These outputs are compared with Target Matrix using Forward propagation algorithms.

8. If desired accuracy is not then we have to feedback using Back propagation Algorithm. In this process all weights to links are adjusted to reduce error.

9. Error is compared with Target matrix if desired output then we fix the present values as output values. This may requires thousands of iterations

Applications of Face Recognition :

1. Performing financial operation. (banking transaction)

2. In health care (storing information about patients identifying new bases)

3. Territory protection (entrance to building, ware houses, laboratories, prisons)

4. Government organization (frontier protection, passport control elections registration)

5. Law systems (checking driver licenses, criminal identification)

This Paper recognizes faces belongs to same person at very simple. It requires only his side profile photo. If any person did any crime and now he grown beard or grown mustaches or any change made by him in his face to escape. This paper recognizes him. So it can be used be used in criminal identification. This is the major application of this paper. The main heart of this paper is RAW image form the RAW image it calculates the inputs. With the front profile it is very difficult to recognize to do this we have to do a lot of things that may require costly hardware devices Scanners, Sensors etc. This requires only side photo with bmp format. The front profile recognition is difficult than the side profile because the photo is two dimensional it is not possible to calculate the input layer parameters from the front profile. By the side profile face is appeared as a convex shapes with nose projected to outside so we can easily calculates the distances and angles with making nose as a origin.

The same concept can also be used to recognize patterns such as recognizing characters, recognizing any particular shapes. This can be used in many applications like in many Commercial transactions, Personal management, Security and the law enforcement applications, especially in criminal identification, Authentication in secure system.

References :

Introduction to Artificial Neural Systems – J.M.Zurada
Elements of Artificial Neural Networks- Kishan Mehrotra,Chelkuri K.Mohan
Neural Computing- Theory and Practice-Waserman

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